Bacterial endophytes as indicators of susceptibility to Cercospora Leaf Spot (CLS) disease in Beta vulgaris L.

The fungus Cercospora beticola causes Cercospora Leaf Spot (CLS) of sugar beet (Beta vulgaris L.). Despite the global importance of this disease, durable resistance to CLS has still not been obtained. Therefore, the breeding of tolerant hybrids is a major goal for the sugar beet sector. Although recent studies have suggested that the leaf microbiome composition can offer useful predictors to assist plant breeders, this is an untapped resource in sugar beet breeding efforts. Using Ion GeneStudio S5 technology to sequence amplicons from seven 16S rRNA hypervariable regions, the most recurring endophytes discriminating CLS-symptomatic and symptomless sea beets (Beta vulgaris L.ssp. maritima) were identified. This allowed the design of taxon-specific primer pairs to quantify the abundance of the most representative endophytic species in large naturally occurring populations of sea beet and subsequently in sugar beet breeding genotypes under either CLS symptomless or infection stages using qPCR. Among the screened bacterial genera, Methylobacterium and Mucilaginibacter were found to be significantly (p < 0.05) more abundant in symptomatic sea beets with respect to symptomless. In cultivated sugar beet material under CLS infection, the comparison between resistant and susceptible genotypes confirmed that the susceptible genotypes hosted higher contents of the above-mentioned bacterial genera. These results suggest that the abundance of these species can be correlated with increased sensitivity to CLS disease. This evidence can further prompt novel protocols to assist plant breeding of sugar beet in the pursuit of improved pathogen resistance.


Results
Bioinformatics analysis of sea beet microbiome. A total of 1,485,067 reads was obtained from the sequenced samples comprising four symptomless and four symptomatic sea beets. The mean number of reads per sample was 185,633 with a range of ± 49,918. Rarefaction analysis reporting the number of observed species across all samples indicated that the diversity was not significantly different between the sample groups evaluated using richness as a parameter (p = 0.97) ( Supplementary Fig. S1). At 97% identity, the reads were classified into 85 OTUs further divided into 29 families, 22 genera, and 38 species using the Greengenes database v13.5 24 and the curated MircoSeq reference library v2013.1 on the Ion Reporter cloud. Further, analysis using QIIME2 25 resulted in 514 amplicon sequence variants (ASVs) which were classified into 35 orders, 47 families, and 70 genera. Evidently, QIIME2-based data analysis resulted in the classification of more genera. However, the classification and representation of major genera remained comparable between both the methods used ( Supplementary  Fig. S2). The outputs from QIIME2 were considered more robust and used for further elaboration.
The bacterial community of the sequenced Beta maritima leaves is presented with a relative abundance plot in Fig. 1A. The genus Sphingomonas was found predominant reaching a percentage of 69.4% in symptomless and 52.5% in symptomatic samples (Fig. 1A). Other predominating genera detected on symptomless and  Fig. S2). The contribution of the abundances of predominant bacterial genera (present > 1%) to the sample phenotypes is visualised as a PCA-biplot in Fig. 1B. The first two principal components explained 57.6% of the variability in the data. The arrows indicate the direction and strength of each variable to the overall distribution. A stronger positive correlation of Methylobacterium and Mucilaginibacter can be seen towards the symptomatic plants. In contrast, the abundances of Pseudomonas and Massilia are less correlated to the symptomatic phenotype. The commonly abundant genera, like Sphingomonas and Aureimonas, show the least contribution to the separation of the phenotypes.
Additionally, linear discriminant analysis (LDA) effect size (LEfSe) analysis revealed differences in the composition of the sea beet microbiome. The results indicated a higher enrichment of Methylobacterium, Hyemenobacter, Mucilaginibacter, and Spirosoma in symptomatic plants and a higher presence of Sphingomonas and Acidovorax in symptomless plants. The trends remain comparable between PCA-biplot and LEfSe analysis for Methylobacterium and Mucilaginibacter ( Fig. 1C and Supplementary Fig. S3).
Validation of bacteria found associated with CLS on naturally occurring sea beet populations. The abundance of Cercospora beticola and of the most distinctively abundant bacteria between the sample groups presented in Fig. 1 were taken for downstream validation on larger sample sizes using Real-Time qPCR with QuantStudio 12 K Flex (Life Technologies, USA). In total 1,512 qPCR reactions were carried out on 504 independent sea beet individuals collected in the years 2019 and 2021 across coastal localities covering 3 countries (Table 1) in triplicates. The number of analysed symptomless and symptomatic plants were comparable, being 288 and 216 specimens respectively.
The relative abundance of Cercospora beticola determined in symptomless and symptomatic sea beet individuals is depicted in Fig. 2. The fungus was detected in all samples irrespective of the phenotype but was found to be significantly higher in symptomatic sea beets for the years 2019 (p < 0.05) and 2021 (p < 0.05). To obtain a parallel insight, we used digital PCR allowing the maximum resolution and sensitivity with assays for Cercospora ( Supplementary Fig. S4). Results confirmed the differences reported by qPCR. The correlation of abundance analyses with the two methods was significant (p < 0.05) for the same.  www.nature.com/scientificreports/ Among the many bacterial taxa tested for which primer sequences are provided in Supplementary Table S1, Methylobacterium and Mucilaginibacter had a significant cycle threshold (Ct) variation (p < 0.05) revealed by qPCR analysis between symptomatic and symptomless samples (Fig. 3) across both the years. This was found to be consistent with the observations from sequencing presented in Fig. 1 and Supplementary Fig. S3, which resulted as the biomarkers enriched in symptomatic plants from LDA Effect Size (LEfSe) analysis. Particularly, the abundance of these bacteria was found to be significantly increased in the symptomatic plants. Validation of bacteria found associated with CLS on field-grown sugar beets. To ascertain and quantify the presence of CLS within 209 sugar beet individuals grown in field conditions, we also targeted the Cercospora beticola rDNA by qPCR (Fig. 4)

Discussion
The occurrence of a given leaf microbiome is related to both the host genetics and environmental conditions. In some species, denser populations of microorganisms have been found in leaf areas more prone to the entry of microbes, such as stomatal opening and nearby leaf veins 26 . Damaging ultraviolet radiation, low levels of water, and nutrients can be stressors for microorganisms causing them to seek refuge inside plants 27 . In the same way, a plant, when undergoing stress, such as drought, nutritional limitations, herbivore damage, or the invasion of pathogens can seek help from microbes by fostering their entrance as endophytes recruiting them at higher www.nature.com/scientificreports/ levels. However, the success of endophytes in protecting the plant depends on the severity of the stress and the genotypic resources of the plant itself 28,29 . It is therefore expected that weaker plants including those that, due to more susceptible genotypic configurations and suffering pathogenic injury would more actively attempt to cope with the stress by invoking microbial endophytes 30 .
In this work, we found substantial differences in the leaf microbiome composition when comparing symptomless and symptomatic sea beets. Particularly, Alphaproteobacteria and Gammaproteobacteria, two large classes of the phylum Proteobacteria were found to be significantly differentially enriched. Their abundance has also been reported in the literature as pre-dominant groups normally present in the leaf microbiome of plants 31,32 . Notably, among Alphaproteobacteria, we found the genera Sphingomonas, Methylobacterium, and Pseudomonas. These three genera accounted for more than 70% of the sequences found through sequencing. Importantly, all three species were significantly more abundant in plants infected by CLS. Other bacteria found less frequently but still significantly overrepresented in the symptomatic plants were from the phyla of the Actinobacteria (Propionibacterium) and Bacteroidetes (Mucilaginibacter). Conversely, the members of the classes Betaproteobacteria (Massilia) and Gammaproteobacteria (Pseudomonas) were low in overall counts and not significantly different in relation to CLS disease.
The presence of Sphingomonas was observed in more than 63% of the samples. This gram-negative bacterium has been studied for its role in environmental remediation owing to its ability to bind heavy metals and enhance the expression of cysteine-rich metallothionein proteins 33 . The Sphingomonas genus, as an endophyte also has an important role in counteracting biotic and abiotic stresses, such as mitigation of salinity stress 34 and protection against leaf-pathogenic Pseudomonas syringae and Xanthomonas campestris 35 . Particularly, Sphingomonas and P. syringae are direct competitors for glucose, fructose, and sucrose. The possible plant-protecting effect of Sphingomonas may be due to their high abundance on the leaf surface since early colonization is an important determinant for an effective biocontrol agent 36 . Its occurrence in plants calling for increased defense is congruent with the plant protective effect of Sphingomonas reported by Innerebner et al. 35 .
The above differences suggest also that some of the inner bacterial dwellers of Beta plants could be better suited to offer defense mechanisms sought for by the plant. In this sense, Methylobacterium appears as the genus that is most significantly differentially abundant between symptomless vs symptomatic and resistant vs susceptible individuals. Besides, it was found to be one of the predominant endophytes from sequencing (22% of the overall sequences). While many endophytes are known to enter from the root apparatus, some can access plants via stomatal openings. In this respect, it is worth remarking that the main ecological niche of the genus Methylobacterium is the phyllosphere. They are typically considered the most abundant bacterial genera ranging between 10 4 and 10 7 colony-forming units per gram fresh weight 37,38 . Strains of Methylobacterium have also been found to improve potato yield under adverse conditions 39 . Another study showed that the Methylobacterium genus fostered plant growth through auxin and cytokine biosynthesis 40 .
While plants seeking protection via endophytic admittance are aimed at higher defense responses, this strategy does not guarantee success in disease avoidance as it is largely influenced by the plant's own genotypic background. This is shown in tomato where Methylobacterium has been found to affect the physiological condition of the plants either positively or negatively 32 . Therefore, as mentioned, the strategy of endophytic recruitment and intensification is not in itself a measure that ensures a guaranteed biocontrol strategy. At this stage, we do not   [41][42][43][44] . It has been described to have roles in plant growth promotion 45,46 . In one of the studies, it was observed that Mucilaginibacter as an endophyte was shown to alleviate salt stress in Arabidopsis 41 . In a very recent and interesting report, a greater relative abundance of Mucilaginibacter in Verticillium dahliae-and Macrophomina phaseolina-infested strawberry cultivars was shown, coherent with our observation 44 .
In this report, the correlation between different species of endophytes concentration and CLS occurrence has been shown both by examining spontaneously growing populations of B. maritima scattered across 15 locations in different countries, and subsequently confirmed in cropped plants using both resistant and susceptible genotype varieties during the CLS infection. The gradient of bacterial target detectability, unfolding in precise agreement with both the disease progression and host susceptibility (Figs. 2, 3, 4, and 5) is supportive of the phenomenon. In interpreting these data, one hypothesis could be that, since CLS development coincides with the progression of summer, the higher content of endophytes could just be part of a general increase in overall plants as a mere seasonal trend. However, this might not be the sole possibility explaining the observed data since endophytes increase in sugar beet breeding lines grown under field conditions is also differentially and statistically higher in the susceptible line when compared at a comparable stage with the resistant variety and thus the latter was indeed effectively less impacted by CLS infection.
Regarding technical considerations on the approach that was followed in this study, we first defined taxa that were consistently featured within the endophytic microbiome of the plants under investigation using by NGS 16 s amplicon sequencing. Having acquired that information, we targeted the relevant candidates using quantitative www.nature.com/scientificreports/ PCR and the same DNA extraction method exploiting the possibility to work simultaneously on large numbers of samples, as in the case of the QuantStudio 12 K Flex, which can process up to 12,000 qPCR samples in the same run. These automated DNA extraction and purification technologies enabled high processivity to the screening and a robust statistical design. As an additional check, we finally explored the performance of digital PCR, which could be recommended in cases where no signal arises from regular qPCR. The sensitivity of digital PCR is 100 -fold higher due to individual amplifications in distributed segments of the chip and specific target-annealing oligonucleotide probes based on TaqMan technology. Additionally, digital PCR is recommended for the absolute quantification of a specific low abundant target 47 and to establish the concentration of a reference target for subsequent use in other platforms like qPCR 48 . This relatively novel technology has been used to quantify Aspergillus species in soils collected from raisin vineyards 49 and to enumerate probiotic strains of Lactobacillus acidophilus and Bifidobacterium animalis replacing the traditional plate counts because of its extreme precision 50 .
In this study, the use of digital PCR has therefore served as a double-check of already obtained data by qPCR based on flanking primers-directed amplification. This technique could be recommended as an alternative to standard qPCR in cases where the abundance of target organisms would be too low to be detected by Real-Time PCR (e.g. those yielding undetermined Ct outputs).
In conclusion, the abundance of a defined species within the plant endophytic microbiome is strongly correlated with a major physio-pathological condition of the plant, in this case specifically with plants infected by Cercospora. This evidence is interpreted as consistent with the notion that stressed plants more crucially seek the help of potentially beneficial microorganisms to increase their chances to cope with the disease. Plants facing environmental abiotic or biotic stresses are known from literature reports to be richer in endophytic taxa compared to unstressed controls. The admittance of endophytes however is not in itself a guarantee that the host would remain disease/stress-free. On the contrary, just like in our healthcare situations, an activated immune reaction, or the fact of undergoing therapy is consistent with the presence of the disease and not with its avoidance. Therefore, an increased load of endophytes is not expected to be directly associated with resistant plant genotypes since such plants need not rely on the trade-off deals with external helpers. What we are observing here suggests that plants that are more prone to CLS disease and loosening barriers for endophyte recruitment, are those which have the lowest level of effectiveness in their genotypic potential of resistance to the pathogen. Therefore, we propose that an assay of endophytic abundance, especially for the genera which resulted differentially featured in symptomless vs symptomatic beet plants (e.g. Methylobacterium and Mucilaginibacter), can be routine screened to serve the needs of breeders seeking markers associated with disease resistance for assets of the commercial seed production in worldwide sugar beet cropping.

Material and methods
Sea beet sampling for sequencing and microbiome analysis. Samples from sea beet used for sequencing analysis were collected in Palmižana (Croatia) and Torcello (Venice, Italy) in August 2018. For each of the two locations, seeds were collected from clearly symptomatic (Palmižana) and symptomless (Torcello) plants (Fig. 6).
Seeds were then sown at the experimental farm at the University of Padova (Legnaro, Italy), in March 2019, to confirm their phenotypic status under natural field conditions. We selected a field with two crop-rotation (sugar beet-wheat) and endemic Cercospora beticola infection which was validated with a soil-based PCR test to ascertain the presence of the fungus. True phenotypes of 60 plants were evaluated between March and August 2019 in Padova, Italy. The experimental design was divided into four randomized blocks, each one divided into four subplots of 2.7 × 10 m dimension. Outside the randomized block, a control plot was placed, and plants were maintained without any treatments. Four leaves from symptomless and four leaves from symptomatic plants were then collected in August 2019. Samples were placed in sterilized 2 ml Eppendorf tubes and carried immediately to the laboratory for DNA extraction. Samples were not frozen to avoid artifacts in the resulting microbiome composition upon metabarcoding 51 . Base-calling and run demultiplexing were performed by Torrent Suite Software version 5.10.0 (Thermo Fisher Scientific, USA) with default parameters. Ion Reporter cloud software version 5.12 (Thermo Fisher Scientific, USA), was adopted to process 16S metagenomic data using default parameters. Taxonomic assignment of unique reads was done using a multi-stage BLAST with the Greengenes v13.5 24 and MicroSEQ 16S reference libraries v2013.1 which in-built databases on Ion Reporter (Thermo Fisher Scientific, USA). At this stage, care was taken to identify and remove reads assigned to chloroplast and mitochondria. To strengthen and support the preliminary analysis using Ion Reporter (Thermo Fisher Scientific, USA), an alternative pipeline was also used to process the data. First, the raw reads were trimmed for 20 base pairs on both ends to remove primers using cutadapt 53 and analysed using QIIME2 v2020.08 25 pipeline. Imported reads were then denoised and dereplicated using qiime dada2 plugin. This was followed by taxonomic classification of ASVs by classify-consensus-blast plugin using Silva SSU v138.1 54 as the reference database. The taxonomy abundance table at genus level was further processed using DESeq2 55 to normalize for the library size. The resultant normalized taxonomy table was filtered for taxas for average reads resulting greater than 10 when combining all samples and used for comparison between the symptomatic and symptomless phenotypes. Taxonomic abundance plots were made using ggplot2 package in R 56 . PCA-biplots were generated using the FactoMineR package in R 57 to visualise the contribution of the most abundant genera to the sample phenotypes. The abundance matrix was also used to develop diversity plots using Calypso web tool 58 . LDA Effect Size (LEfSe) analysis 59 was performed on the abundance matrix obtained from QIIME2. The genera resulting differentially abundant were considered as targets for downstream validation using qPCR to correctly ascertain the association of the bacteria to the phenotype.
Primer design for PCR-based validation. The 16S rRNA sequences of the seven most recurring and differentially abundant bacterial taxa were used to design primers to be used in quantitative PCR. The ribosomal DNA sequence of Cercospora beticola (NCBI accessions MF681167.1, MF681115.1, and AY840527.2) was also used to design qPCR primers. The software Primer Express v3.0 (Thermo Fisher Scientific, USA) was used to pick the suitable forward and reverse oligomers. The primer pair sequences and the corresponding targeted bacteria are shown in Table 2.
Sea beet sampling for qPCR validation from spontaneous plant populations. The subsequent sampling campaigns to gather material to be analyzed by quantitative PCR targeting specific bacteria were car-   Table 1 reports the locations and number of samples collected from each site. Leaves were placed in a 50 ml Falcon tube, stored on ice, and transferred to the laboratory for DNA extraction. A map with all the sampling locations pinned is presented in Fig. 7 to visually appreciate the span of sampling.   Declaration for plant sampling. The sampling of plants was carried out following the Regulation (EU) 2016/2031 on protective measures against plant pests ("Plant Health Law"), and all permissions needed to collect "leaf samples" of Beta vulgaris L. were obtained.

Data availability
The datasets generated during the current study are available from the corresponding author on reasonable request.